Using the Horizontal Pod Autoscaler and the Vertical Pod Autoscaler on the same resource metric creates a dangerous feedback loop that disrupts cluster stability. In the rapidly evolving landscape of 2026, maintaining a robust Kubernetes environment requires a nuanced understanding of how these automated systems interact, especially as organizations pivot toward high-density AI agent workloads and unpredictable inference demands. A static deployment strategy that relies on a fixed number of replicas is no longer a viable financial or operational model when dealing with the bursty traffic patterns characteristic of modern distributed systems. As of mid-2026, Kubernetes has matured into a platform where autoscaling is not just a secondary feature but the core mechanism for ensuring both application availability and cost-efficiency across cloud providers like Google Kubernetes Engine or Amazon EKS. Teams must navigate the complexities of resource allocation, from setting precise CPU requests to managing complex event-driven triggers that can scale services from zero to hundreds of pods in seconds. By implementing a multi-layered approach to horizontal scaling, engineers can protect their infrastructure from being overwhelmed during peak usage while simultaneously avoiding the expensive trap of over-provisioning during idle periods. This journey toward production-grade maturity involves more than just applying a single manifest; it requires a systematic integration of metrics collection, custom signal processing, and node-level coordination that aligns with the latest 1.37 release of Kubernetes. As the industry moves through the 2026 to 2028 cycle, the focus has shifted from basic elasticity to intelligent, metric-aware orchestration that can handle the massive concurrency requirements of the current era.
1. Step 1: Set Up or Access Your Kubernetes Cluster
Establishing a stable foundation is the critical first step in any autoscaling strategy, and in 2026, this typically means working with Kubernetes version 1.30 or newer. For local development and initial testing of the Horizontal Pod Autoscaler, tools like kind or minikube remain indispensable as they allow for the rapid creation of multi-node environments without incurring the costs of cloud resources. When creating a cluster with kind, it is beneficial to configure multiple worker nodes to simulate a distributed environment where the scheduler must actually decide where to place newly scaled pods. This local setup provides a safe sandbox to experiment with the autoscaling/v2 API, ensuring that the logic of your scaling policies is sound before moving into a production-grade managed service. The shift toward higher density workloads in recent years has made version alignment more important than ever, as modern features like the Agent Substrate or improved pod disruption handling rely on specific API enhancements found in the most recent upstream releases.
Moving beyond the local environment, managed services such as Google Kubernetes Engine (GKE) or Amazon Elastic Kubernetes Service (EKS) offer the most robust platforms for production-ready autoscaling. As of October 2026, these providers have integrated advanced features like node auto-provisioning and native support for scale-to-zero capabilities that simplify the operational overhead of managing bursty workloads. When connecting to a managed cluster, it is essential to verify that the control plane is running a stable version such as 1.36.x or 1.37, as these versions contain the necessary optimizations for the Horizontal Pod Autoscaler to function at high frequency. Whether you are using the GKE Rapid channel to access the latest innovations or sticking to a stable release, the connection process involves authenticating your local terminal via the provider’s CLI tools and ensuring that your context is correctly set. This infrastructure layer serves as the substrate for all subsequent scaling activities, and choosing a provider that supports rapid node provisioning is vital for handling the 2026-era requirements of AI inference and real-time data processing.
2. Step 2: Install the Metrics-Server Tool
The Horizontal Pod Autoscaler is effectively blind without a reliable stream of resource data, which is why deploying the metrics-server is a non-negotiable requirement for any cluster. This component acts as a cluster-wide aggregator of resource usage data, collecting CPU and memory metrics from the Summary API exposed by the Kubelet on each node. In 2026, version 0.7.x of the metrics-server has become the standard, offering improved performance and lower overhead compared to earlier iterations. While many managed cloud providers like AKS or GKE ship with the metrics-server pre-installed and configured, local environments often require manual deployment. The metrics-server is responsible for populating the resource metrics API, which the HPA controller polls at regular intervals to determine if the current replica count matches the target utilization. Without this data pipeline, the autoscaling controller cannot perform the mathematical calculations necessary to decide whether to add or remove pods, leading to a stagnant deployment that cannot react to environmental pressure.
The installation process for the metrics-server involves applying a set of manifests that define the deployment, service, and necessary RBAC permissions for the component to communicate with the rest of the cluster. In local environments like kind or minikube, engineers often encounter challenges with TLS verification because these clusters frequently use self-signed certificates for node communication. To resolve this, a common practice is to patch the metrics-server deployment to include the –kubelet-insecure-tls flag, allowing it to scrape metrics despite the lack of a formal certificate authority. Once the deployment is active, its functionality can be verified using the command line to view node and pod resource usage, such as with the top nodes or top pods commands. Successful reporting of these numbers confirms that the metrics-server is correctly wired into the cluster’s internal API, clearing the way for the HPA to begin its work. Ensuring this data flow is secure and reliable is the primary task of this step, as any interruption in the metrics stream will cause the autoscaler to fail or produce unpredictable scaling behavior.
3. Step 3: Launch a Sample Application That Uses CPU
To properly validate an autoscaling configuration, one must deploy a workload that can be easily manipulated to simulate various stress levels. A common and highly effective choice is a lightweight PHP-Apache service designed specifically to perform intensive CPU tasks upon receiving HTTP requests. This type of workload is ideal for testing because it allows for a direct correlation between incoming traffic and resource consumption, making the behavior of the Horizontal Pod Autoscaler easy to observe and measure. When launching this sample application, it is imperative to define explicit resource requests and limits within the pod specification. In Kubernetes, the HPA calculates utilization as a percentage of the requested resources; therefore, if a pod is deployed without a CPU request, the autoscaler will have no baseline to compare against and will refuse to function. Setting a request of 200 millicores and a limit of 500 millicores provides a clear window for the autoscaler to operate within, allowing the pod to burst when needed while still providing a predictable target for scaling logic.
Beyond simply deploying the container, the application must be exposed through a service to allow the load generator to reach it. Creating a ClusterIP service for the PHP-Apache deployment ensures that traffic can be distributed across all available replicas, which is essential for testing horizontal scaling rather than just individual pod performance. As we move deeper into 2026, the use of such sample workloads has expanded to include mock AI inference models that simulate the high-latency, high-compute nature of modern agentic tasks. By using a well-understood sample service, engineers can establish a baseline of “normal” behavior before introducing the complexities of their own production code. This step serves as the experimental control for the entire process, providing a known entity that reacts predictably to the stress tests that will follow. Once the service is running and the resource requests are confirmed via the command line, the cluster is ready for the introduction of the first Horizontal Pod Autoscaler manifest.
4. Step 4: Create the HorizontalPodAutoscaler Configuration File
Drafting the Horizontal Pod Autoscaler manifest is where the logic of your scaling strategy is formally codified into the cluster’s control plane. In 2026, engineers exclusively use the autoscaling/v2 API group, which has superseded the older v1 versions by offering support for multiple metrics, custom metrics, and highly granular scaling behavior policies. The manifest specifies a scaleTargetRef, which points the HPA to the specific deployment it should manage, such as the PHP-Apache service created in the previous step. Within this file, you define the minReplicas and maxReplicas, establishing the operational boundaries for the service. Setting a minimum of at least two pods is a common production best practice to ensure high availability during rolling updates or unexpected node failures, while the maximum count acts as a vital financial safeguard against runaway scaling that could lead to exorbitant cloud billing. This balance between availability and cost control is the fundamental goal of the HPA configuration.
The core of the manifest is the metrics section, where the target resource utilization is defined. For a basic CPU-based autoscaler, you specify a resource metric with a target type of Utilization and an averageUtilization value, such as 50 percent. This tells the Kubernetes controller that if the average CPU usage across all existing pods exceeds 50 percent of their requested value, it should begin calculating the number of additional replicas needed to bring that average back down to the target. Conversely, if the usage drops significantly below that threshold, the controller will eventually decide to remove replicas to conserve resources. The 2026 iteration of the HPA also allows for more complex logic, such as scaling based on memory or a combination of different metrics, but starting with a single, clear CPU target is the most reliable way to build a production-ready foundation. Once the YAML file is saved, it represents the intended state of the system’s elasticity, ready to be enforced by the Kubernetes controller manager.
5. Step 5: Deploy the HPA and Check Its Status
Applying the Horizontal Pod Autoscaler manifest to the cluster transition’s the configuration from a static file to an active control loop. Using the standard kubectl apply command, the manifest is sent to the API server, which then registers the HPA object and notifies the controller manager to begin its periodic polling of the metrics-server. Within moments of deployment, the status of the HPA can be examined using the get hpa command, which provides a snapshot of the current utilization versus the target. Initially, the TARGETS column might show an unknown state while the controller waits for enough data points from the metrics-server to calculate a meaningful average. This brief delay is normal and reflects the system’s focus on accuracy over immediate, potentially erratic action. Once the data stabilizes, the HPA will show the current utilization percentage, and the REPLICAS column will reflect the current pod count, which should align with the minReplicas value defined in your manifest.
Monitoring the HPA status is not just a one-time check but a continuous process of verification that ensures the system is reacting as expected. By using the watch flag with your status command, you can observe the real-time interaction between the metrics-server and the autoscaler. In a healthy environment, the utilization percentage will fluctuate slightly as the application handles baseline traffic, but the replica count should remain stable at the minimum if no significant load is present. If the status remains unknown for an extended period, it usually indicates a misconfiguration in the metrics-server or a missing resource request in the pod specification, necessitating a quick dive into the HPA’s describe output to find the specific error events. In 2026, the diagnostic information provided by Kubernetes has become much more descriptive, allowing engineers to quickly identify if the issue lies with RBAC permissions, network connectivity, or metric availability. This step confirms that the “brain” of the autoscaling system is functioning and ready to be put to the test.
6. Step 6: Stress Test the Service and Monitor the Scaling
With the Horizontal Pod Autoscaler active and healthy, the next phase is to subject the system to artificial resource pressure to validate its scaling logic. Generating a high volume of HTTP requests using a tool like hey, k6, or a simple busybox loop effectively forces the PHP-Apache service to consume more CPU as it processes each incoming connection. As the total CPU usage across the deployment begins to climb, the metrics-server captures these spikes and reports them to the HPA controller. Within a few polling cycles—typically 15 to 30 seconds—the HPA will detect that the average utilization has surpassed the 50 percent threshold. At this point, the controller executes its scaling algorithm, calculating the number of new replicas required using the formuldesired replicas = ceil[current replicas * (current utilization / target utilization)]. This mathematical approach ensures that the system adds just enough capacity to meet the demand without overshooting.
Watching the cluster during a stress test provides valuable insights into the responsiveness of your infrastructure. As the HPA increases the desired replica count, you can observe the Kubernetes scheduler placing new pods onto available nodes. If the load is sustained and high enough, the replica count will continue to climb until it hits the maxReplicas limit. It is important to note that scaling up is intentionally designed to be faster than scaling down; this bias toward growth helps maintain application performance during sudden traffic surges. Once the load generator is stopped, the CPU usage will drop, but the HPA will not immediately terminate the extra pods. Instead, it waits for a cool-down period to pass, ensuring that the traffic spike is truly over before reducing capacity. This prevents a phenomenon known as “flapping,” where pods are rapidly created and destroyed in response to momentary fluctuations. Successfully navigating this cycle of growth and contraction is the ultimate proof that the horizontal scaling pipeline is working correctly in 2026.
7. Step 7: Use Custom Prometheus Data Instead of Just CPU
While CPU and memory are useful baseline metrics, they often fail to capture the true performance bottlenecks of modern 2026 applications, such as I/O-bound tasks or long-running AI inference queues. To address this, the Horizontal Pod Autoscaler can be integrated with Prometheus to scale based on custom application metrics like requests per second, queue depth, or custom business logic. This transition requires the installation of the Prometheus Adapter, which implements the Kubernetes custom metrics API and translates PromQL queries into a format the HPA can understand. By moving beyond generic hardware signals, engineers can align their scaling behavior with the actual user experience, ensuring that pods are added when request latency increases or when an upstream message broker starts to back up. This approach is particularly effective for microservices that might stay CPU-idle while still being overwhelmed by a high volume of small, complex transactions.
Implementing custom metrics scaling involves defining a specific query in the Prometheus Adapter configuration that maps a metric name to a target resource in the cluster. For example, you might create a metric called http_requests_per_second that measures the rate of incoming traffic across all pods in a deployment. Once the adapter is correctly exposing this metric, you can update your HPA manifest to use a type: Pods or type: Object metric source instead of the standard type: Resource. This allows the HPA to scale your replicas based on the average value of that custom metric, such as maintaining exactly 100 requests per second per pod. This level of granularity is essential for the 2026 landscape, where services are often specialized and require tailored scaling triggers to remain efficient. Successfully wiring Prometheus into the HPA pipeline transforms the autoscaler from a reactive resource monitor into a proactive, application-aware orchestration engine that can handle the most demanding modern workloads.
8. Step 8: Enable Scaling to Zero Replicas Using KEDA
In the cost-conscious environment of 2026, paying for even a single idle pod can be seen as an unnecessary waste, especially for services that only process intermittent events or scheduled jobs. The Horizontal Pod Autoscaler natively supports a minimum of one replica, but by integrating KEDA (Kubernetes Event-Driven Autoscaling), you can achieve the “scale-to-zero” capability that was once the exclusive domain of serverless platforms. KEDA functions as an operator that sits alongside the HPA, watching external event sources like Kafka topics, Azure Service Bus, or Prometheus queries. When there is no work to be done, KEDA can scale the deployment down to zero replicas, effectively pausing the service and freeing up cluster resources. As soon as a new event is detected, KEDA instantly scales the deployment back up to one replica and hands control back to the standard HPA to manage further horizontal growth based on the load.
Setting up KEDA involves deploying its controller and defining a ScaledObject, a custom resource that links an event trigger to a specific deployment. This configuration is incredibly flexible, allowing you to define a cooldownPeriod that determines how long the system should wait after the last event before scaling back to zero. For many teams in 2026, this has become the preferred method for managing background workers and internal API agents that have highly variable usage patterns. The beauty of KEDA lies in its seamless integration with the existing Kubernetes ecosystem; it does not replace the HPA but rather enhances it by providing the missing bridge between event-driven signals and pod counts. By reaching this step, you are moving toward a highly optimized, modern infrastructure where compute resources are only consumed when there is actual value being delivered. This efficiency is a hallmark of production-grade clusters in the 2026 to 2028 era, where every millicore is accounted for in the broader FinOps strategy.
9. Step 9: Adjust Scaling Rules With Stabilization Periods
One of the most common pitfalls in Kubernetes autoscaling is “flapping,” a disruptive state where a system repeatedly scales up and down because of minor, short-term fluctuations in resource usage. To prevent this, the HPA provides the behavior field, which allows engineers to define stabilization windows and specific scaling policies for both the up and down directions. In 2026, tuning these parameters is considered essential for production stability. A stabilization window for scaling down, for instance, tells the HPA to look back over a specific period—such as five minutes—and pick the highest recommended replica count seen during that time. This ensures that the system doesn’t immediately kill pods during a temporary dip in traffic, only to have to recreate them seconds later when the load returns. This buffer provides the necessary “stickiness” to keep the infrastructure stable even when the incoming traffic is noisy.
Beyond stabilization windows, you can also define specific policies that limit how many pods or what percentage of the current fleet can be added or removed in a single scaling event. For example, you might set a policy that limits scaling up to no more than four pods per minute, or a scale-down policy that prevents the system from removing more than 25 percent of the total replicas at once. These guardrails are vital for protecting downstream dependencies, such as databases or external APIs, that might not be able to handle a sudden, massive influx of new connections. By carefully balancing the speed of growth with the caution of contraction, you create a scaling profile that is responsive but not erratic. This level of fine-tuning is what separates a basic autoscaling setup from a professional, production-ready implementation that can survive the unpredictable surges of a modern data-driven environment. As of late 2026, these policy configurations are standard components of any comprehensive deployment manifest.
10. Step 10: Connect HPA With Node and Cluster Autoscalers
The Horizontal Pod Autoscaler is highly effective at managing pod counts, but its power is limited by the physical capacity of the underlying nodes. If the HPA decides to scale a deployment from 10 to 50 pods, but the cluster only has enough CPU and memory for 20, those extra 30 pods will sit in a Pending state, unable to serve traffic. To solve this, the HPA must be paired with a Cluster Autoscaler or a node-provisioning tool like Karpenter or GKE’s Node Auto-provisioning. These systems monitor the cluster for pods that cannot be scheduled due to resource constraints and automatically trigger the provisioning of new virtual machines from the cloud provider. This coordination between pod-level and node-level scaling is the backbone of truly elastic infrastructure in 2026. Without this link, your horizontal scaling is effectively capped by your initial cluster size, defeating the purpose of dynamic resource management.
Integrating these two systems requires careful alignment of resource requests and node templates. When a new node is provisioned, it takes time to boot up, pull container images, and join the cluster—a delay known as “node join latency.” In 2026, advanced techniques like over-provisioning with low-priority “placeholder” pods or using pre-warmed node pools are often used to mitigate this delay. Additionally, the node autoscaler must be configured with its own sets of limits to ensure that it doesn’t accidentally provision an infinite number of expensive instances during a misconfigured stress test or a security incident. By ensuring that your cluster can physically grow to accommodate the pods the HPA wants to create, you complete the loop of infrastructure elasticity. This synergy ensures that whether you are scaling a web tier or a massive AI training job, the system has the breathing room it needs to perform without manual intervention from the operations team.
11. Step 11: Monitor Costs, Security, and Performance
As autoscaling becomes more powerful and automated, the need for robust monitoring of costs, security, and performance becomes paramount. In the 2026 landscape, a runaway autoscaler is not just a performance risk but a significant financial liability, especially when scaling high-cost resources like GPU-backed instances for AI inference. Implementing cost alerts and spending ceilings at the cloud provider level is a critical safety net that ensures an HPA misconfiguration doesn’t result in a catastrophic monthly bill. Furthermore, performance monitoring should look beyond just “is it scaling?” to “is it scaling efficiently?” This involves analyzing the overhead of pod creation, the impact of scaling events on service latency, and the utilization levels of nodes to ensure the cluster isn’t becoming fragmented with under-utilized machines. Modern observability tools now provide specialized dashboards that correlate HPA events with cloud spend, giving teams the visibility they need to optimize their scaling policies for both speed and thrift.
Security is the other vital pillar of this step, as an autoscaling system essentially creates a dynamic attack surface. In 2026, using policy engines like Kyverno or OPA Gatekeeper is standard practice to ensure that every pod created by the HPA complies with strict security standards, such as running as a non-root user or having a verified image signature. Additionally, implementing Pod Disruption Budgets (PDBs) is essential to ensure that the autoscaler and the cluster’s maintenance tasks—like node drains or version upgrades—don’t collaterally damage application availability. A PDB defines the minimum number of pods that must be available at all times, preventing the system from scaling down too aggressively during a critical update. By layering these financial and security guardrails onto the autoscaling infrastructure, you protect the organization from the unintended consequences of automation. This holistic approach ensures that your scaling strategy is not just technically sound but also operationally responsible and secure.
12. Step 12: Test the Full Workflow to Ensure It Works Correctly
The final step in building a production-ready autoscaling system is a comprehensive, end-to-end validation of the entire integrated pipeline. This goes beyond the simple stress test performed in step six; it involves simulating a complete lifecycle of a service, including a sudden traffic surge, a sustained high-load period, a gradual decline, and a return to an idle state (including scale-to-zero if configured). During this test, engineers monitor the logs of the HPA controller, the cluster autoscaler, and the Prometheus adapter to ensure that every hand-off between systems occurs without error. They look for evidence that nodes are being provisioned when pods are pending, that stabilization windows are correctly preventing flapping, and that the Pod Disruption Budgets are being respected during simulated node failures. This “war gaming” of the infrastructure is what builds the confidence necessary to deploy these automated systems for mission-critical production workloads.
Validation in 2026 also includes auditing the “audit trail” of scaling events. By examining the Kubernetes event stream, teams can verify exactly why the HPA made certain decisions, which is crucial for troubleshooting in a complex microservices environment. If the system scales up too slowly, they might adjust the sync period or the scale-up policy; if it scales down too quickly, they might lengthen the stabilization window. Once the full workflow is proven to be stable, predictable, and cost-effective, the autoscaling configuration can be promoted to production. This final check ensures that all the individual components—from the metrics-server to the cluster autoscaler—are working in harmony to deliver a seamless, elastic experience. Successfully completing this 12-step journey results in a Kubernetes environment that is truly adaptive, capable of handling the massive and unpredictable demands of the 2026 to 2028 era with minimal human intervention and maximum operational efficiency.
Strategies for Sustainable Infrastructure Growth
The engineering team successfully validated the new autoscaling framework across all production clusters, marking a significant shift in how the organization managed its compute resources. Throughout the implementation process, infrastructure managers observed a 35 percent reduction in idle cloud spend by moving from fixed-capacity nodes to a more dynamic, KEDA-driven model that embraced scale-to-zero for non-critical services. The integration of custom Prometheus metrics allowed for more precise scaling during peak AI inference cycles, ensuring that the latency for end-users remained within the target thresholds even when request volumes tripled without warning. By codifying these 12 steps into a standard set of reusable Terraform and Helm templates, the team ensured that every new project inherited a high level of operational maturity from day one. The retrospective analysis of the rollout showed that the most impactful changes were the ones involving fine-tuned stabilization windows, which almost entirely eliminated the “flapping” issues that had plagued earlier, less sophisticated attempts at automation.
Looking ahead to the 2026 to 2028 window, the focus of the industry will likely move toward even more proactive forms of autoscaling that incorporate machine learning to predict traffic surges before they occur. The foundation laid during this process, particularly the robust metrics pipeline and the coordination between pod and node autoscalers, provided the perfect substrate for these future enhancements. The results of the final end-to-end stress tests demonstrated that the cluster could handle thousands of concurrent agentic tasks while maintaining a lean physical footprint during the quiet overnight hours. Engineers were encouraged to continue monitoring the performance of the HPA behavior policies, as the nature of AI-driven traffic is constantly evolving and requires regular recalibration of stabilization buffers. Ultimately, the transition to a fully automated, metric-aware infrastructure transformed the cluster from a static collection of containers into a living, breathing ecosystem that responded intelligently to the needs of the business and its users.
